Learning Lane Graph Representations for Motion Forecasting

Liang, Ming; Yang, Bin; Hu, Rui; Chen, Yun; Liao, Renjie; Feng, Shijie; Urtasun, Raquel

doi:10.1007/978-3-030-58536-5_32

Cited by 333 publications

(343 citation statements)

References 27 publications

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“…Accuracy refers to the switching method's ability to predict whether the end-to-end predictor will have high error. Performance refers to the resulting hybrid predictor (which uses planning-based when σ = 1) error, measured as average distance error (ADE) between the predicted trajectories and the ground-truth ones, which is standard in motion prediction [1,2,26,24,16]. Hypotheses.…”

Section: Methodsmentioning

confidence: 99%

“…When choosing the function class for these learned predictors, high capacity models are very appealing. Recent progress has shown that we can train deep neural networks end-to-end to go from a history of raw state information or even raw sensor data to a distribution over predicted trajectories for a human, implicitly or explicitly extracting relevant features, identifying potential targets in the scene, computing trajectories for each, and assessing their relative likelihoods [2,16,26,24]. Such models dominate the leaderboards in benchmarks for motion prediction (or "forecasting", as it is sometimes referred to) like Argoverse [3] or INTERACTION [25].…”

Section: Introductionmentioning

confidence: 99%

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On complementing end-to-end human behavior predictors with planning

Sun¹,

Jia²,

Dragan³

2021

Robotics: Science and Systems XVII

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High capacity end-to-end approaches for human motion (behavior) prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events. Planningbased prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift (as we verify in this work), but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal. In this work, we analyze one family of approaches that strive to get the best of both worlds: use the end-to-end predictor on common cases, but do not rely on it for tail events / out-of-distribution inputsswitch to the planning-based predictor there. We contribute an analysis of different approaches for detecting when to make this switch, using an autonomous driving domain. We find that promising approaches based on ensembling or generative modeling of the training distribution might not be reliable, but that there very simple methods which can perform surprisingly well -including training a classifier to pick up on tell-tale issues in predicted trajectories.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On complementing end-to-end human behavior predictors with planning

Sun¹,

Jia²,

Dragan³

2021

Robotics: Science and Systems XVII

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show abstract

“…In 2019, attention models were introduced in the field of trajectory prediction [112][113][114][115][116][117]. In [113,118,119], the attention module is used to prevent to pre-define an exact graph structure of a given traffic situation.…”

Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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Towards the aim of mastering level 5, a fully automated vehicle needs to be equipped with sensors for a 360∘ surround perception of the environment. In addition to this, it is required to anticipate plausible evolutions of the traffic scene such that it is possible to act in time, not just to react in case of emergencies. This way, a safe and smooth driving experience can be guaranteed. The complex spatio-temporal dependencies and high dynamics are some of the biggest challenges for scene prediction. The subtile indications of other drivers’ intentions, which are often intuitively clear to the human driver, require data-driven models such as deep learning techniques. When dealing with uncertainties and making decisions based on noisy or sparse data, deep learning models also show a very robust performance. In this survey, a detailed overview of scene prediction models is presented with a historical approach. A quantitative comparison of the model results reveals the dominance of deep learning methods in current state-of-the-art research in this area, leading to a competition on the cm scale. Moreover, it also shows the problem of inter-model comparison, as many publications do not use standardized test sets. However, it is questionable if such improvements on the cm scale are actually necessary. More effort should be spent in trying to understand varying model performances, identifying if the difference is in the datasets (many simple situations versus many corner cases) or actually an issue of the model itself.

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“…Bruna et al [83] develop the graph convolution based on the spectral graph theory. In [84], the graph kernels, whereby graphs or nodes can be embedded into the feature space using a mapping function, is proposed to study the random walk problem [85] . More recently, researchers find that Euclidean data and sequential data can also be represented by special graphs.…”

Section: Remove Some Edgesmentioning

confidence: 99%

Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals

Zhang

Jiang

Fan

et al. 2021

Int. J. Autom. Comput.

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Recently, deep learning has achieved great success in visual tracking tasks, particularly in single-object tracking. This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning. First, we introduce basic knowledge of deep visual tracking, including fundamental concepts, existing algorithms, and previous reviews. Second, we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures. Then, we conclude with the general components of deep trackers. In this way, we systematically analyze the novelties of several recently proposed deep trackers. Thereafter, popular datasets such as Object Tracking Benchmark (OTB) and Visual Object Tracking (VOT) are discussed, along with the performances of several deep trackers. Finally, based on observations and experimental results, we discuss three different characteristics of deep trackers, i.e., the relationships between their general components, exploration of more effective tracking frameworks, and interpretability of their motion estimation components.

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Learning Lane Graph Representations for Motion Forecasting

Cited by 333 publications

References 27 publications

On complementing end-to-end human behavior predictors with planning

On complementing end-to-end human behavior predictors with planning

A Review on Scene Prediction for Automated Driving

Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals

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